Selected Contributions in Data Analysis and Classification

  • Paula Brito
  • Guy Cucumel
  • Patrice Bertrand
  • Francisco de Carvalho

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Analysis of Symbolic Data

    1. Front Matter
      Pages 1-1
    2. Paula Brito
      Pages 13-22
    3. Costantina Caruso, Donato Malerba
      Pages 23-33
    4. Patrick J. F. Groenen, Suzanne Winsberg
      Pages 55-67
    5. André Hardy, Joffray Baune
      Pages 69-81
    6. Georges Hébrail, Yves Lechevallier
      Pages 83-94
    7. Monique Noirhomme-Fraiture, Etienne Cuvelier
      Pages 103-111
    8. Haralambos Papageorgiou, Maria Vardaki
      Pages 113-122
    9. Rosanna Verde, Antonio Irpino
      Pages 123-134
  3. Clustering Methods

    1. Front Matter
      Pages 135-135
    2. Jean-Pierre Barthélemy, Gentian Gusho, Christophe Osswald
      Pages 137-150
    3. Vladimir Batagelj, Anuška Ferligoj, Patrick Doreian
      Pages 151-159
    4. Hans-Hermann Bock
      Pages 161-172
    5. Tristan Colombo, Alain Guénoche
      Pages 193-201
    6. Gérard Govaert, Mohamed Nadif
      Pages 203-212
    7. Melvin F. Janowitz
      Pages 213-223
    8. Robert Stanforth, Evgueni Kolossov, Boris Mirkin
      Pages 225-233
    9. Javier Trejos-Zelaya, Mario Villalobos-Arias
      Pages 235-244
  4. Conceptual Analysis of Data

    1. Front Matter
      Pages 245-245
    2. Richard Emilion
      Pages 247-258
    3. Marianne Huchard, Amedeo Napoli, Mohamed Rouane Hacene, Petko Valtchev
      Pages 259-270
    4. Ryszard S. Michalski, William D. Seeman
      Pages 285-297
  5. Consensus Methods

    1. Front Matter
      Pages 307-307
    2. Bruno Leclerc
      Pages 317-324
    3. Fred R. McMorris, Robert C. Powers
      Pages 325-329
  6. Data Analysis, Data Mining, and KDD

    1. Front Matter
      Pages 331-331
    2. Wolfgang Gaul
      Pages 357-366
    3. Régis Gras, Pascale Kuntz
      Pages 367-376
    4. David J. Hand
      Pages 377-386
    5. Tu-Bao Ho
      Pages 387-396
    6. Géraldine Polaillon, Laure Vescovo, Magali Michaut, Jean-Christophe Aude
      Pages 397-408
    7. Henri Ralambondrainy, Jean Diatta
      Pages 409-417
    8. Djamel Abdelkader Zighed
      Pages 419-430
  7. Dissimilarities: Structures and Indices

    1. Front Matter
      Pages 431-431
    2. Samia Aci, Gilles Bisson, Sylvaine Roy, Samuel Wieczorek
      Pages 433-444
    3. Casper J. Albers, Frank Critchley, John C. Gower
      Pages 445-454
    4. Patrice Bertrand, François Brucker
      Pages 455-464

About this book


By inviting me to write a preface, the organizers of the event in honour of Edwin Diday, have expressed their a?ection and I appreciate this very much. This gives me an opportunity to express my friendship and admiration for Edwin Diday, and I wrote this foreword with pleasure. My ?rst few meetings withEdwinDidaydatebackto1965through1975,daysofthedevelopmentof French statistics. This was a period when access to computers revolutionized the practice of statistics. This does not refer to individual computers or to terminals that have access to powerful networks. This was the era of the ?rst university calculation centres that one accessed over a counter. One would deposit cards on which program and data were punched in and come back a few hours or days later for the results. Like all those who used linear data analysis, the computer enabled me to calculate for each data set the value of mathematical objects (eigenvalues and eigenvectors for example) whose optimality properties had been demonstrated by mathematicians. It was - ready a big step to be able to do this in concrete experimental situations. With Dynamic Clustering Algorithm, Edwin Diday allowed us to discover that computers could be more than just a way of giving numerical values to known mathematical objects. Besides the e?ciency of the solutions he built, he led us to integrate the access to computers di?erently in the research and practice of data analysis.


Random variable algorithms classification cluster analysis clustering data analysis data mining knowledge discovery knowledge management learning multivariate statistics operations research optimization principal component analysis statistics

Editors and affiliations

  • Paula Brito
    • 1
  • Guy Cucumel
    • 2
  • Patrice Bertrand
    • 3
  • Francisco de Carvalho
    • 4
  1. 1.Faculty of EconomicsUniversity of PortoPortoPortugal
  2. 2.ESG UQAMMontrealCanada
  3. 3.Department LussiENST BretagneCesson-Sévigné CedexFrance
  4. 4.Centre of Computer Science (CIn)Federal University of Pernambuco (UFPE)Recife-PEBrazil

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